Predicting acute and chronic diabetes complications among adults newly diagnosed with diabetes: development and validation of dynamic risk prediction models.

Authors

Rozalina G McCoy, Shrinath Patel, Louis Faust, Herbert C Heien, Nan Zhang, Brian Caffo, Che Ngufor

Published in

Nature communications. Volume 17. Issue 1. Aug 25, 2026. Epub Aug 25, 2026.

Abstract

Diabetes affects one in ten adults worldwide, yet clinicians lack tools to simultaneously predict which complications a given patient is most likely to develop in the near term. Existing risk models typically address single complications, were developed in specialized cohorts, and use heterogeneous time horizons, limiting their utility for individualized clinical decision-making. Here, we show that machine learning models trained on nationwide U.S. administrative claims data for 400,400 adults with newly diagnosed type 1 or type 2 diabetes can accurately predict the monthly risk of nine acute and chronic complications (cardiovascular disease, cerebrovascular disease, peripheral vascular disease, nephropathy, neuropathy, retinopathy, and hypoglycemic and hyperglycemic crises) with predictions that update dynamically as new clinical data become available. Using Regularized Logistic Regression (GLMnet) and Extreme Gradient Boosting (XGBoost) with walk-forward temporal validation, we demonstrate strong discrimination in external validation against an independent electronic health record cohort (areas under the receiver operating characteristic curve 0.77-0.85 across complications for the best-performing model). The Diabetes Complications Risk Calculator therefore provides encounter-specific near-term risk estimates for multiple diabetes complications using routinely available clinical data, offering a potential framework for personalized risk stratification that could support shared decision-making and population health management across healthcare settings. The generalizability of the Diabetes Complications Risk Calculator to other cohorts will need to be tested.

PMID:
42778552
Bibliographic data and abstract were imported from PubMed on 24 Sep 2026.

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